scholarly journals An approach for experiment evaluations for multiple harvests crops based on non-linear regression

2021 ◽  
Vol 39 (3) ◽  
pp. 250-257
Author(s):  
Alessandro Dal’Col Lúcio ◽  
Maria Inês Diel ◽  
Bruno G Sari
Keyword(s):  
Linear Regression ◽  
Growth Models ◽  
Fruit Production ◽  
Production Cycle ◽  
Total Production ◽  
Biological Processes ◽  
Production Potential ◽  
Non Linear ◽  
Using Data ◽  
The Moment

ABSTRACT Biologically based growth models can be an alternative in identifying the productive response of multiple harvest vegetables. By interpreting the estimates of the parameters of the models, it is possible to estimate the total production, the rate of fruit production, and the moment when the crop reaches its maximum production potential. Besides, by estimating confidence intervals, these responses can be compared between genotypes or between different treatments. Therefore, the purpose of this manuscript is to present a literature review, and a detailed step-by-step, to interpreting the evolution of the production cycle of vegetables with multiple harvests crops based on non-linear regression. All the requirements that must be met in this type of analysis were presented in detail based on non-linear regression, providing the necessary steps for this type of analysis in details. Demonstration is given using data from strawberry cultivation along with the associated R scripts and interpretation of analysis output in material supplemental. This approach can allow for more relevant inferences than standard means analyses through better examination and modeling of the underlying biological processes.

Age determination and growth in wild Petrogale lateralis pearsoni and captive Petrogale lateralis 'MacDonnell Ranges race'

2004 ◽  
Vol 52 (4) ◽  
pp. 447 ◽  
Author(s):  
Michelle Jones ◽  
David Taggart ◽  
Peter Temple-Smith
Keyword(s):  
Growth Rate ◽  
Growth Models ◽  
Age Determination ◽  
Growth Curves ◽  
Tammar Wallaby ◽  
Length Growth ◽  
Non Linear ◽  
In The Wild ◽  
Using Data ◽  
Linear Growth Model

Accurate assessment of age is important for effective captive husbandry techniques and assists in understanding developmental processes, population dynamics, reproductive strategies and seasonal breeding. Using linear and non-linear regression, this study analysed the growth rate of the head and pes length of known-age, captive-born pouch young of the black-footed rock-wallaby, Petrogale lateralis 'MacDonnell Ranges race'. Growth curves for head and pes length from the captive-born pouch young were then used to predict the age of pouch young of P. lateralis pearsoni using data collected from the field. Observations on the development of the eyes, ears and body of P.�lateralis 'MacDonnell Ranges race' were also recorded. Results showed that a non-linear growth model best described the head-length growth of captive-born pouch young (r2 = 99.5%), whereas logistic regression was the most accurate predictor of pes-length growth (r2 = 99.6%). No significant differences were found when the two growth models were applied to head and pes data from wild pouch young, suggesting that the growth models derived from captive animals can be used to accurately predict the age of pouch young in the wild. During a preliminary cross-fostering trial, we examined growth of the head and pes length in pouch young of P. lateralis 'MacDonnell Ranges race' that had been cross-fostered onto the teats of surrogate tammar wallaby (Macropus eugenii) mothers; comparisons were made to the growth rate of pouch young of the same race that had remained with their natural mothers.

scholarly journals Modeling the COVID-19 pandemic - parameter identification and reliability of predictions

2020 ◽  
Author(s):  
Klaus Hackl
Keyword(s):  
United States ◽  
Linear Regression ◽  
South Korea ◽  
Parameter Identification ◽  
The United States ◽  
Epidemic Models ◽  
Non Linear ◽  
The Future ◽  
History Of ◽  
Using Data

AbstractIn this paper, we try to identify the parameters for two elementary epidemic models, the so-called SI- and SIS-models, via non-linear regression using data of the COVID-19 pandemic. This is done based on the data for the number of daily infections. Studying the history of predictions made, we attempt to estimate their reliability concerning the future course of the epidemic. We validate this procedure using data for the case numbers in China and South Korea. Then we apply it in order to find predictions for Germany, Italy and the United States. The results are encouraging, but no final judgment on the validity of the procedure can yet be made.

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

Protected Culture Systems for Strawberry Production

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 459d-459
Author(s):  
Fumiomi Takeda ◽  
Paul R. Adler ◽  
D. Michael Glenn
Keyword(s):  
Light Intensity ◽  
Fruit Size ◽  
Fruit Production ◽  
Hydroponic Culture ◽  
Production Cycle ◽  
Soilless Culture ◽  
Labor Costs ◽  
Culture Systems ◽  
Nutrient Film Technique ◽  
Plant Densities

Strawberry plants (cvs. Camarosa, Chandler, Sweet Charlie, Primetime, Jewel, and Tribute) were grown in soilless culture systems in a greenhouse from October to May. Fresh-dug and runner-tip Aplug® plants were transplanted into two systems: vertically stacked pots (24 plants/m2) containing perlite and horizontal nutrient film technique troughs (13 plants/m2). Plants were fertigated continuously with recirculating nutrient solution. In a 7-month production cycle, the plug plants bloomed earlier and produced more fruit during the first month of harvest (December) than the fresh-dug plants. Higher yields from plug plants were a result of more fruit numbers and not larger fruit size. Fruit production averaged 6.0 and 3.5 kg/m2 in the trough and pot systems, respectively. The vertical growing system allows greater plant densities, but light intensity reaching the plants in the lower sections of the tower can be less than 20% of levels measured at the top. Establishment costs of protected culture systems are higher, but production is earlier and labor costs are typically reduced. Greenhouse hydroponic culture systems could extend the winter strawberry production to more northern locations.

scholarly journals Precommercial Thinning Increases Spruce Yields in Boreal Mixedwoods in Alberta, Canada

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 412
Author(s):  
Ivan Bjelanovic ◽  
Phil Comeau ◽  
Sharon Meredith ◽  
Brian Roth
Keyword(s):  
Growth Models ◽  
Growth And Yield ◽  
Diameter Growth ◽  
Growth Responses ◽  
Projection System ◽  
Precommercial Thinning ◽  
Thinning Intensity ◽  
Boreal Mixedwoods ◽  
Using Data ◽  
Mixedwood Stands

A few studies in young mixedwood stands demonstrate that precommercial thinning of aspen at early ages can improve the growth of spruce and increase stand resilience to drought. However, information on tree and stand responses to thinning in older mixedwood stands is lacking. To address this need, a study was initiated in 2008 in Alberta, Canada in 14 boreal mixedwood stands (seven each at ages 17 and 22). This study investigated growth responses following thinning of aspen to five densities (0, 1000, 2500, 5000 stems ha−1 and unthinned (control)). Measurements were collected in the year of establishment, and three and eight years later. Mortality of aspen in the unthinned plots was greater than in the thinned plots which were not significantly different amongst each other. Eight years following treatment, aspen diameter was positively influenced by thinning, while there was no effect on aspen height. The density of aspen had no significant effect on the survival of planted spruce. Spruce height and diameter growth increased with both aspen thinning intensity and time since treatment. Differentiation among treatments in spruce diameter growth was evident three years from treatment, while differentiation in height was not significant until eight years following treatment. Yield projections using two growth models (Mixedwood Growth Model (MGM) and Growth and Yield Projection System (GYPSY)) were initialized using data from the year eight re-measurements. Results indicate that heavy precommercial aspen thinning (to ~1000 aspen crop trees ha−1) can result in an increase in conifer merchantable volume without reducing aspen volume at the time of harvest. However, light to moderate thinning (to ~2500 aspen stems ha−1 or higher), is unlikely to result in gains in either deciduous or conifer merchantable harvest volume over those of unthinned stands.

scholarly journals Is There a Relationship between the Elasticity of Brain Tumors, Changes in Diffusion Tensor Imaging, and Histological Findings? A Pilot Study Using Intraoperative Ultrasound Elastography

2021 ◽  
Vol 11 (2) ◽  
pp. 271
Author(s):  
Santiago Cepeda ◽  
Sergio García-García ◽  
María Velasco-Casares ◽  
Gabriel Fernández-Pérez ◽  
Tomás Zamora ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Diffusion Tensor Imaging ◽  
Linear Regression ◽  
Brain Tumors ◽  
Diffusion Tensor ◽  
Intraoperative Ultrasound ◽  
Ultrasound Elastography ◽  
Proliferation Index ◽  
Strong Positive Correlation ◽  
Non Linear

Intraoperative ultrasound elastography (IOUS-E) is a novel image modality applied in brain tumor assessment. However, the potential links between elastographic findings and other histological and neuroimaging features are unknown. This study aims to find associations between brain tumor elasticity, diffusion tensor imaging (DTI) metrics, and cell proliferation. A retrospective study was conducted to analyze consecutively admitted patients who underwent craniotomy for supratentorial brain tumors between March 2018 and February 2020. Patients evaluated by IOUS-E and preoperative DTI were included. A semi-quantitative analysis was performed to calculate the mean tissue elasticity (MTE). Diffusion coefficients and the tumor proliferation index by Ki-67 were registered. Relationships between the continuous variables were determined using the Spearman ρ test. A predictive model was developed based on non-linear regression using the MTE as the dependent variable. Forty patients were evaluated. The pathologic diagnoses were as follows: 21 high-grade gliomas (HGG); 9 low-grade gliomas (LGG); and 10 meningiomas. Cases with a proliferation index of less than 10% had significantly higher medians of MTE (110.34 vs. 79.99, p < 0.001) and fractional anisotropy (FA) (0.24 vs. 0.19, p = 0.020). We found a strong positive correlation between MTE and FA (rs (38) = 0.91, p < 0.001). A cubic spline non-linear regression model was obtained to predict tumoral MTE from FA (R2 = 0.78, p < 0.001). According to our results, tumor elasticity is associated with histopathological and DTI-derived metrics. These findings support the usefulness of IOUS-E as a complementary tool in brain tumor surgery.

scholarly journals Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 299
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Trend ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Linear Regression Models ◽  
Non Linear ◽  
Nonlinear Trends ◽  
The Impact ◽  
Non Parametric

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.

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